U.S. patent number 11,395,022 [Application Number 17/338,609] was granted by the patent office on 2022-07-19 for passenger preference based content delivery in commercial passenger vehicles.
This patent grant is currently assigned to PANASONIC AVIONICS CORPORATION. The grantee listed for this patent is Panasonic Avionics Corporation. Invention is credited to Rahul Chachare, Rita Chen, Victor Salov.
United States Patent |
11,395,022 |
Chachare , et al. |
July 19, 2022 |
Passenger preference based content delivery in commercial passenger
vehicles
Abstract
Vehicle entertainment systems can determine an entertainment
preference of a passenger based on the interactions of the
passenger with media devices on board the vehicle. The interactions
can include how the passenger rates content, whether the passenger
views the entirety of the content, or information regarding the
passenger such as frequent flier data. The analysis to determine
the preferences of the passenger are done without placing cookies
on the passenger's devices and by components on board the vehicle.
The analysis can include machine learning techniques that build
trained models of passenger preferences. Additionally, the trained
models can develop profiles for each passenger. At the end of the
travel experience, the preferences of a passenger can be deleted
such that a new passenger does not see content that was based on
the prior passenger's preferences.
Inventors: |
Chachare; Rahul (Lake Forest,
CA), Salov; Victor (Lake Forest, CA), Chen; Rita
(Lake Forest, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Panasonic Avionics Corporation |
Lake Forest |
CA |
US |
|
|
Assignee: |
PANASONIC AVIONICS CORPORATION
(Irvine, CA)
|
Family
ID: |
1000005680894 |
Appl.
No.: |
17/338,609 |
Filed: |
June 3, 2021 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N
20/00 (20190101); H04N 21/252 (20130101); H04N
21/4756 (20130101); H04N 21/2146 (20130101); H04N
21/41422 (20130101); H04N 21/44204 (20130101); H04N
21/239 (20130101) |
Current International
Class: |
H04N
21/25 (20110101); H04N 21/442 (20110101); H04N
21/414 (20110101); H04N 21/214 (20110101); H04N
21/239 (20110101); G06N 20/00 (20190101); H04N
21/475 (20110101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
|
|
|
|
|
|
|
104361502 |
|
Feb 2015 |
|
CN |
|
2014072931 |
|
May 2014 |
|
WO |
|
Other References
Mehta, R. M., "A Prediction Model of Airline Passenger Preference:
Identifying Factors That Predict Passenger Preference Between Low
Cost and Legacy Carriers," Dissertation submitted to College of
Aeronautics Florida Institute of Technology, Sep. 2017. cited by
applicant.
|
Primary Examiner: Montoya; Oschta I
Attorney, Agent or Firm: Perkins Coie LLP
Claims
What is claimed is:
1. A method of processing data, the method comprising: performing,
by a server on a commercial passenger vehicle, in a first portion
of a predetermined nominal duration, a training to obtain an
entertainment preference of at least one passenger, wherein
performing the training further comprises: sending, by the server,
to a media playback device associated with the at least one
passenger and on board the commercial passenger vehicle, a
multimedia content; and in response to the media playback device
displaying at least a portion of the multimedia content during a
second portion of the predetermined nominal duration, receiving, by
the server, from the media playback device, an interaction by the
at least one passenger on the multimedia content, wherein the
performing the training and receiving the interaction are performed
with duration centric tracking of the media playback device; and
updating, by the server, based on the received interaction, the
entertainment preference for the at least one passenger to obtain
an updated entertainment preference; wherein updating the
entertainment preference further comprises: determining, by the
server, one or more passengers onboard the commercial passenger
vehicle with entertainment preferences similar to the at least one
passenger; identifying, by the server, other multimedia content
which has not been displayed by the media playback device and has
been viewed by the one or more passengers; and determining, by the
server, a probability of the at least one passenger viewing the
other multimedia content.
2. The method of claim 1, wherein performing the training further
comprises: tracking the multimedia content being displayed on the
media playback device; applying machine learning algorithms to
develop a trained model, wherein the trained model is operable to
determine a similarity between the multimedia content and the
entertainment preference; and in response to determining the
similarity between the multimedia content and the entertainment
preference, determining whether to update the entertainment
preference or continue to send multimedia content associated with
the entertainment preference to the media playback device.
3. The method of claim 1, further comprising: prompting, by the
server, the at least one passenger, to rate the multimedia content
being displayed on the media playback device; determining, by the
server, based on the rating, whether the multimedia content is
indicative of the entertainment preference; and in response to
determining whether the multimedia content is indicative of the
entertainment preference, determining whether to update, by the
server, the entertainment preference or to continue, by the server,
to send multimedia content associated with the entertainment
preference to the media playback device.
4. The method of claim 1, wherein updating the entertainment
preference further comprises: applying item-based and/or user-based
techniques.
5. The method of claim 1, wherein the entertainment preference
includes a length of the multimedia content.
6. The method of claim 1, wherein the predetermined nominal
duration is based on a scheduled flight time of an airplane.
7. The method of claim 1, further comprising: deleting, by the
server, at an end of the predetermined nominal duration, the
updated entertainment preference for the at least one
passenger.
8. The method of claim 1, wherein the entertainment preference of
the at least one passenger is obtained based on any of: (1) social
analytics or press releases; (2) an interaction of the at least one
passenger with social media content and/or the multimedia content;
(3) an origin and/or destination of the commercial passenger
vehicle; and/or (4) the predetermined nominal duration.
9. The method of claim 1, wherein the interaction is based on any
of whether the at least one passenger fast forwards and/or rewinds
over portions of the multimedia content, and/or a rating the at
least one passenger gives the multimedia content.
10. The method of claim 1, wherein the entertainment preference is
obtained from performing a plurality of trainings to obtain a
plurality of entertainment preferences of a plurality of
passengers, wherein the plurality of passengers is collocated with
each other and the at least one passenger.
11. A system implemented on a commercial passenger vehicle, the
system comprising: a processor located in the commercial passenger
vehicle; and a memory located in the passenger commercial vehicle,
the memory having instructions stored thereon that, when executed
by the processor, cause the processor to: perform, in a first
portion of a predetermined nominal duration, a training to obtain
an entertainment preference of at least one passenger, wherein
performing the training further comprises: sending, to a media
playback device associated with the at least one passenger and on
board the commercial passenger vehicle, a multimedia content; in
response to the media playback device displaying at least a portion
of the multimedia content during a second portion of the
predetermined nominal duration, receive, from the media playback
device, an interaction by the at least one passenger on the
multimedia content, wherein the performing the training and
receiving the interaction are perform with duration centric
tracking of the media playback device; and update, based on the
received interaction, the entertainment preference for the at least
one passenger to obtain an updated entertainment preference; and
transmit the updated entertainment preference for the at least one
passenger to the memory; wherein the instructions further cause the
processor to: determine one or more passengers onboard the
commercial passenger vehicle with entertainment preferences similar
to the at least one passenger; identify other multimedia content
which has not been displayed by the media playback device and has
been viewed by the one or more passengers; and determine a
probability of the at least one passenger viewing the other
multimedia content.
12. The system of claim 11, wherein the media playback device is a
mobile device that belongs to the at least one passenger, or
wherein the media playback device is located behind a head rest of
a seat in the commercial passenger vehicle which is facing the at
least one passenger.
13. The system of claim 11, wherein the media playback device is
collocated with a plurality of media playback devices, and wherein
the entertainment preference is obtained by performing a plurality
of trainings to obtain a plurality of entertainment preferences of
a plurality of passengers associated with the plurality of media
playback devices.
14. The system of claim 11, wherein the instructions further cause
the processor to: apply machine learning algorithms to develop a
trained model, wherein the trained model is operable to determine a
similarity between the multimedia content and the entertainment
preference.
15. The system of claim 11, wherein the entertainment preference of
the at least one passenger is obtained based on any of (1) social
analytics or press releases; (2) an interaction of the at least one
passenger with social media content; (3) an origin and/or
destination of the commercial passenger vehicle; and/or (4) the
predetermined nominal duration.
16. A non-transitory computer-readable medium containing
instructions, execution of which in a computer system on board a
commercial passenger vehicle causes the computer system to:
perform, in a first portion of a predetermined nominal duration, a
training to obtain an entertainment preference of at least one
passenger, wherein performing the training further comprises:
sending, to a media playback device in the commercial passenger
vehicle and associated with the at least one passenger, a
multimedia content; in response to the media playback device
displaying at least a portion of the multimedia content during a
second portion of the predetermined nominal duration, receive, from
the media playback device, an interaction by the at least one
passenger on the multimedia content, wherein the performing the
training and receiving the interaction are perform with duration
centric tracking of the media playback device; and update, based on
the received interaction, the entertainment preference for the at
least one passenger to obtain an updated entertainment preference;
and transmit the updated entertainment preference for the at least
one passenger to a server, wherein the server is on board the
commercial passenger vehicle; wherein the entertainment preference
is updated by: determining, by the computer system, one or more
passengers onboard the commercial passenger vehicle with
entertainment preferences similar to the at least one passenger;
identifying, by the computer system, other multimedia content which
has not been displayed by the media playback device and has been
viewed by the one or more passengers; and determining, by the
computer system, a probability of the at least one passenger
viewing the other multimedia content.
17. The non-transitory computer readable medium of claim 16,
wherein performing the training further comprises: applying machine
learning algorithms to develop a trained model, wherein the trained
model is operable to determine a similarity between the multimedia
content and the entertainment preference.
18. The non-transitory computer readable medium of claim 16,
wherein the media playback device is collocated with a plurality of
media playback devices, and wherein the entertainment preference is
obtained by performing a plurality of trainings to obtain a
plurality of entertainment preferences of a plurality of passengers
associated with the plurality of media playback devices.
19. The non-transitory computer readable medium of claim 16,
wherein the entertainment preference is updated by applying
item-based and/or user-based techniques.
20. The non-transitory computer readable medium of claim 16,
wherein the instructions include instructions causing the computer
system to delete, at an end of the predetermined nominal duration,
the updated entertainment preference for the at least one
passenger.
Description
TECHNICAL FIELD
This application is related to delivering content to passengers on
commercial vehicles, and more particularly, to delivering passenger
preference based content.
BACKGROUND
Commercial travel has evolved to provide entertainment options to
passengers traveling to their destinations. For example, in an
airplane or train, entertainment options are provided on monitors
located on the back of seats, where the monitors can enable
passengers to watch movies or television shows as they travel to
their destinations. The monitors can also provide travel related
information to the passengers. For example, passengers can view a
map with the current location of the airplane or train and an
estimated time of arrival to their destinations. Thus, in-vehicle
entertainment systems can be designed to provide passengers with a
positive travel experience.
BRIEF DESCRIPTION OF THE DRAWINGS
The techniques introduced here may be better understood by
referring to the following Detailed Description in conjunction with
the accompanying drawings, in which like reference numerals
indicate identical or functionally similar elements.
FIG. 1 shows an exemplary airplane with an entertainment system
installed.
FIG. 2A shows an airplane interacting with an exemplary machine
learning based preference system.
FIG. 2B depicts two exemplary techniques for optimizing an
advertisement
FIG. 3 shows multiple seat back monitors operating with a
server.
FIG. 4 shows an exemplary system for presenting passenger
preference based content.
FIGS. 5A-B show two exemplary actions from a passenger to indicate
a preference.
FIG. 6 shows an exemplary flowchart of a method for delivering
passenger preference based content in a commercial passenger
vehicle.
FIG. 7 shows an overview of a system for providing passenger
preference based content on a commercial passenger vehicle.
DETAILED DESCRIPTION
Currently, airplanes or other commercial vehicles use conventional
in-vehicle entertainment systems to broadcast audio or video
content to seatback devices located on the rear of the seat or to
personal electronic devices (PEDs) (e.g., smartphone, laptops, or
tablets) that belong to passengers. The audio or video content may
include movies, television shows, or other content such as
advertisements or flight safety video. Each seatback device has an
enclosure that can have a processor executing custom software
programs to receive messages or commands from an edge server and to
display visual content on a display of the seatback device and to
output sound to a headphone jack. Conventional in-vehicle
entertainment systems can also wirelessly transmit audio or video
content to PEDs that belong to passengers.
Conventional in-vehicle entertainment systems have several
technical drawbacks, a few of which are discussed herein.
Generally, the problems arise from the desire better the travel
experience of passengers. First, the audio or video content
transmitted by conventional in-vehicle entertainment systems to
seatback device and/or PEDs does not frequently change. For
example, the audio or video content stored on a server in the
vehicle is usually updated once a month in part because the audio
or video content is not dependent on the passengers' preferences.
Thus, in the above example, an airplane having a conventional
in-vehicle entertainment system may present to its passengers the
same audio or video content over a course of a month.
Second, the in-vehicle entertainment systems are designed to
provide all passengers traveling on a same airplane or ship with
the same set of audio or video content. Thus, conventional
in-vehicle entertainment systems present a same limited set of
options to passengers to watch or listen to a limited set of audio
or video content. For instance, irrespective of the passenger's
preferences (e.g., language, genre), the audio and video content
are the same.
Third, conventional in-vehicle entertainment systems are not
designed to account for passenger preference related information
that can be available from external sources (e.g., social media) or
prior to the flight (e.g., frequent flier status). Thus, similar to
the issues discussed above, by not accessing information related to
a passenger from a variety of sources, the passenger is presented
is generic audio and video content. Similarly, and fourth,
conventional in-vehicle entertainment systems do not request
passenger feedback nor take into account passenger feedback when
presenting audio and visual content.
Lastly, conventional in-vehicle entertainment systems are
configured to communicate frequently with ground systems or
satellites (e.g., servers) to analyze and obtain data. For
instance, an entertainment system onboard an airplane is updated
with new instructions and new content when the airplane is, for
example, at a terminal or while being wirelessly connected to a
satellite. Thus, over-the-air updates may occur often, such as when
there is a wireless connection to a ground system or satellite.
Accordingly, this application describes a commercial passenger
vehicle entertainment system to overcome at least the above
described technical drawbacks with conventional in-vehicle
entertainment systems. In particular, introduced herein is a
commercial passenger vehicle entertain system that provides
passenger preferences based content to the passenger.
The example headings for the various sections below are used to
facilitate the understanding of the disclosed subject matter and do
not limit the scope of the claimed subject matter in any way.
Accordingly, one or more features of one example section can be
combined with one or more features of another example section.
This patent document describes the exemplary vehicle entertainment
systems in the context of a commercial passenger vehicle such as an
airplane for ease of description. The exemplary vehicle
entertainment systems could be employed in other types of
commercial passenger vehicle such as a train, a ship, or a bus.
Environment
FIG. 1 shows an exemplary vehicle entertainment system 100
installed in an airplane 102. The vehicle entertainment system 100
includes an edge server 106 (or head-end server) located in the
airplane 102. The edge server 106 is communicably coupled to the
seatback devices 104 and personal electronic devices (PEDs) 112 to
provide multimedia contents (e.g., audio, video, image, webpage,
etc.) to the seatback devices 104 and/or PEDs. For example, the
edge server 106 includes a content module (shown as 710 in FIG. 7)
that may send multimedia contents to seatback devices 104 via an
Ethernet switch, and the content module may send multimedia
contents to PEDs 112 via one or more wireless access points 110.
The content module of the edge server 106 can send a list of
multimedia contents to be displayed on a graphical user interface
(GUI) of the seatback devices 104 and/or the PEDs 112.
Based on a passenger's preference, a particular multimedia content
can be selected by the edge server 106 for display on setback
devices 104. A seatback device and/or a PED can be considered a
media playback device at least because the seatback device or a PED
can display or play the multimedia content. The seatback devices
104 and PEDs 112 can include appropriate audio or video codecs
stored thereon to play the multimedia contents provided by the edge
server 102 or another device (e.g., media hard drive) located
onboard the airplane 102.
In some embodiments, the edge server 106 is in communication with
the seatback device 104 and PEDs 112 to obtain data regarding the
preference of a passenger. For example, the seatback device 104 can
prompt a passenger to rate a multimedia content or provide the
option to skip viewing the multimedia content. And, based on the
rating the edge server 106 can determine a preference of the
passenger. The preference can be, for example, whether the
passenger would prefer to view similar multimedia content. In
another example, if the user decides to skip viewing the multimedia
content, the edge server 106 can determine that the user does not
prefer to view similar multimedia content. As such, the edge server
106 can communicate with the seatback devices 104 and/or PEDs 112
to determine a preference of the passenger.
In some cases, the edge server 106 can be pre-loaded with a
prediction of passenger preference. The edge server 106 can
advantageously obtain from a ground server information about
passengers so that the edge server 106 can, based on such
information, provide customized entertainment options to
passengers. For example, when the airplane 102 is waiting at an
airport to board passengers or while the passengers are boarding
the airplane 102, the edge server 106 can obtain from the ground
server a list of predicted preferences about passengers that are
located in or are expected to board the airplane. The ground server
116 may store the list of predicted preferences for the passengers
in a database 108. The database 108 can be stored in the ground
server. In addition, the edge server 106 can be in communication
with a ground server through satellites (for example, when at high
altitude, flying over a body of water, or area where there is
limited signaling from the ground) via an antenna 120.
The list of passenger preferences may include information about
passengers that may have been collected by the airlines and/or by a
third-party (e.g., a social media platform). A list of passenger
preferences may include a table that contains the names of each
passenger that is expected to board the airplane 102, one or more
predicted entertainment preferences for each passenger, and
optionally seat number assigned to each passenger. One or more
predicted entertainment preferences for a passenger may include any
one of or more of the following entertainment categories preferred
by the passenger: movies, music, television shows, on-line training
classes (e.g., Udemy, Codecademy, edx, Coursera, Skillshare,
Udacity, and the like), and news content (e.g., business, sports,
politics, stock prices). The list of passenger preferences for a
passenger may include additional or alternative entertainment
categories derived based on an analysis of the passenger's personal
information (e.g., career or age) and/or based on overall
entertainment related trends from prior passengers who have
travelled on the same or similar travel route.
The edge server 106 can include a passenger module that can obtain
the list of passenger preferences from the ground server and/or
obtain the passenger preferences from the analysis by the edge
server 106. The passenger module can send to the content module the
seat numbers of the passengers and the associated one or more
predicted entertainment preferences of the passengers so that the
content module can send commands or messages to the appropriate
seatback devices 104 to display entertainment options tailored to
the passengers. For example, if a high rating by a passenger
indicates that the passenger enjoys western movies and football,
the content module can send a command to the seatback device
located in front of the passenger to display information about one
or more western movies (if one or more western movies are stored on
the edge server 106 and/or database 108) and to display football
related news. A seatback device 104 can display on a GUI
information about one or more entertainment options based on the
one or more predicted entertainment preferences of the passenger
that sits behind and operates that seatback device. The edge server
106 can store the data indicative of passenger preferences in the
database 108. The database 108 can be stored in the edge server
106.
In some embodiments, the edge server 106 can determine the
passenger preferences based on the passenger's interactions (e.g.,
rating, skipping of content) with the PEDs 112 and/or the seatback
devices 104. Further, the edge server 106 can rely on multiple
sources to determine the preference. For example, the edge server
106 (including content module) can send commands to a PED 112
associated with the passenger that interacted with the PED 112 to
provide a rating, to show entertainment content based on the
rating. For example, when a passenger first starts using his or her
PED on the airplane 102, the passenger may enter his or her seat
number or name via the GUI on the PED, and the PED can send such
information along with the PED's identifier (e.g., MAC address or
IP address) to the passenger module of the edge server 106. Based
on the received seat number or name of the passenger and the
obtained list of passenger preferences, the passenger module can
associate one or more predicted entertainment preferences of the
passenger with the PED operated by the passenger. The passenger
module can send the one or more predicted entertainment preferences
and the associated PED identifiers to the content module.
Over time (e.g., the duration of the travel), rather than relying
on the list of passenger preferences, the edge server 106 can
prompt the PED 112 to interact with the passenger. Based on the
interactions, the edge server 106 can update the multimedia content
being shown. In other words, the list of passenger preferences can
determine the multimedia content that is played when the passenger
first boards the vehicle. Over time, as the passenger interacts
with the PEDs, more data indicative of the passenger's preference
is obtained by the edger server 106. The edge server 106 can, then,
provide updated multi media content.
For instance, a seatback device or PED can obtain from a passenger
a request to display an entertainment option based on the one or
more predicted entertainment preferences. Continuing with the
example described above, based on a message received from the
content module to show one or more entertainment options, the
seatback device or PED can present on a GUI selectable icons for
one or more western movies and football related news. The
selectable icons may be designed to allow the passenger to select a
movie to be played or to read or watch news related to football.
When a passenger selects an entertainment option, the seatback
device or PED can send to the edge server 106 a message that
includes the selected entertainment option so that the edge server
106 can provide or enable the selected content to be displayed on
the seatback device or PED. The passenger module of the edge server
106 stores the list of passenger preferences in the database 108.
Thus, the passenger module can update the predicted entertainment
preferences stored in the database 108 for a passenger based on the
passenger selected entertainment option received in the message
from a seatback device.
As mentioned above, in some embodiments, the selectable icons on
the GUI can also enable a passenger to indicate whether he or she
prefers the displayed entertainment options. In an example
implementation, a selectable icon for a displayed entertainment
option include one or more selectable passenger preference
indicators such as a "like" and/or "dislike" button(s) displayed
adjacent to (e.g., top or bottom of) the selectable icon. If a
seatback device or PED receives indications via its GUI that a
passenger "likes" a western movie and "dislikes" another western
movie, the seatback device or PED can send to the passenger module
of the edge server 106 a message that includes such updated
preferences. In another example implementation (as shown FIG. 5B),
a star rating system can be used to determine the preference. Since
the passenger module stores the list of passenger preferences in
the database 108, the passenger module can update the predicted
entertainment preferences stored in the database 108 for a
passenger based on the one or more updated preferences received in
the message from a PED.
The edge server 106 can update the one or more predicted
entertainment preferences of passengers stored in the database 108
based on receiving messages that indicate whether passengers have
selected entertainment options to be displayed or whether
passengers have provided updated preferences. In some embodiments,
the edge server 106 can update the list of passenger preferences
in-flight based on entertainment related selections indicated by
the passengers. The edge server 106 may transmit to the ground
server 116 via the antenna 114 the updated list of passenger
preferences so that the ground server 116 can update the list of
passenger preferences stored on database 118. For example, after
the airplane 102 has landed at its destination, the edge server 106
may transmit the updated list of passenger preferences to the
ground server 116. In some embodiments, the edge server 106 can
transmit the updated entertainment preference of one or more
passengers to the ground server 116 so that the ground server 116
can update the list of passenger preferences stored on the database
on the ground.
Accordingly, the components of FIG. 1 obtain passenger preferences
(e.g., from a list and/or through passenger interactions) and
provide passenger preference based content to the seatback device
and/or PED associated with the passenger. As mentioned above, a
predicted preference list of one or more passengers can be obtained
from a ground server. For example, when an airplane is at a
terminal, the airplane can communicatively couple to the ground
server to receive the preference list. The preference list can be
based on, for example, prior travel, social media, etc.
Additionally or alternatively, a passenger's preferences can be
determined during the travel time, as the passenger interacts with
an associated seatback device and/or PED. For instance, the
passenger can be prompted to rate a multimedia content and/or given
an option to skip viewing the multimedia content. Based on these
interactions, the edge server 106 can determine the preferences of
the passenger.
Determining Passenger Preference(s)
FIG. 2A shows an airplane 202 interacting with an exemplary machine
learning based preference system 204. The components within
preference system 204 are depicted merely as exemplary components.
Thus, it should be understood that the preference system 204 can
operate with more or fewer modules and that each module can
interact with any other module of the preference system 204.
Moreover, although FIG. 2 depicts preference system 204 as being
external to the airplane 202 (e.g., such as a ground system), it
should be understood that the preference system 204 can be on board
a commercial vehicle such as airplane 202.
Data collection module 206, in some embodiments, obtains data
indicative of a passenger's preferences. For instance, the data
collection module 206 communicates with the seatback device on
airplane 202 associated with a particular passenger to obtain the
interactions of the passenger with the seatback device. The
interactions can, for example, include that the passenger highly
rated a video about currents global news. The seatback device
(e.g., seatback devices 104 in FIG. 1) can communicate the rating
to the data collection module 206 for storage.
The machine learning (ML) training module 208 communicates with the
data collection module 206 to retrieve data that help the ML
training module 208 train the preference system 204. The training
can include using the data stored within the data collection module
206 to build models in order to make predictions or decision
regarding the multimedia content to be displayed to a passenger.
For example, the ML training module 208 can apply collaborative
filtering techniques. Collaborative filtering is the process of
filtering for information or patterns (e.g., multimedia content
viewing preferences) using techniques involving collaboration among
multiple sources. Collaborative techniques include, for example,
user-based and item-based techniques. User-based techniques include
finding users with similar patterns as the target user and
item-based techniques include calculating a similarity between the
items that target users rates and/or interacts with and other
items.
For example, user-based techniques can include finding the
similarity between a target user and other users based on the
preferences obtained by data collection module 206. The target user
can, for instance, be similar with other users inside a commercial
vehicle because the parties prefer to watch sports-related
advertisements. In some embodiments, the user-based techniques can
include different weightages for similarities between users. In
some cases, the ML training module 208 may determine that a target
user is similar to some users and not similar to others. In this
case, the users with which the target user has similarities, may be
given more weightage. For instance, the ML training module 208 may
give more weightage to the content viewed by these users, than the
others. By giving the content more weight, the target user may be
more likely to be presented content that these users have also
viewed.
Item-based techniques can include finding similarities between two
or more items of content (e.g., advertisements). For example, if a
user views the entirety of an advertisements, the ML training
module 208 can determine similar content to present to the user.
This is unlike user-based techniques because the ML training module
208 need not assess the characteristics of the user; rather, the ML
training module 208 assess the characteristics of the
advertisement. For example, a user may view the entire
advertisement for headphones. Subsequently, the ML training module
208 can look for other advertisements that have similar
characteristics to the headphone advertisement. The characteristics
can include, for example, subject matter, audio (e.g., music),
and/or length.
The ML training module 208 can be training using, for example,
neural networks, singular value decomposition (SVD), and/or matrix
factorization (MF). By using matrix factorization, for instance,
the ML training module 208 can decompose a user-item interaction
matrix into the product of two lower dimensionally rectangular
matrices. Matrix factorization can include several sub-techniques
such as Funk MF, SVD++, asymmetric SVD, group-specific SVD, hybrid
MF, and deep learning MF. The ML training module 208 can use one or
more of these techniques to optimize the advertisement.
Optimization can include, for example, instructing another module
(e.g., advertisement management module 214) to transmit portions of
an advertisement to the display of a user and/or skip, fast
forward, and/or rewind advertisements being displayed to a
user.
Further, there are several other techniques that are used to
optimize (e.g., determine similar patterns between users) content.
For example, Bayesian networks, clustering models, latent semantic
models, probabilistic latent semantic models, multiple
multiplicative factor models, latent Dirichlet allocation models,
and Markov decision models. In an airplane setting, for example,
there are a particular passenger that the ML training module 208 is
working to determine preferences for. To do so, the data collection
module 206 can send data of all the passengers on the airplane 202
to the ML training module 208. The ML training module 208 can then
find a set of passengers whose patterns (e.g., ratings) are similar
to the target passenger. Subsequently, the ML training module 208
can determine, based on the patterns of the set of passengers,
multimedia content for the target passenger. To further elaborate,
a target passenger may highly rate an advertisement for a beach
front resort. The ML training module 208, upon receiving the rating
information, can retrieve usage data of passenger that also highly
rated the advertisement. Based on the usage data, the ML training
module 208 can predict the preferences of the target passenger.
In some embodiments, the ML training module 208 can also use the
collaborative filtering based approach to predict preferences when
passengers skip viewing content. For example, a cosine similarity
function can be used to fill-in values that are missing due to the
passenger skipping content. An exemplary cosine similarity function
is below:
.function..function..fwdarw..fwdarw..fwdarw..times..cndot..times..fwdarw.-
.fwdarw..fwdarw. ##EQU00001##
The ML training module 208 can, in some cases, communicate with the
decision making module 210. Alternatively or additionally, the
decision making module 210 can communicate directly with the data
collection module 206. For example, if the data collection module
206 contains a list of passenger preferences, as provided by a
ground server, for example, the decision making module 210 can
obtain the list. The decision making module 210 then determines the
multimedia content to be displayed for the target passenger. For
example, the decision making module 210 can receive a preference
prediction from the ML training module 208 and a list of multimedia
content that is available on plane 202 from data collection module
206. The list of multimedia content can include classifiers for
each multimedia content. The classifiers can be, for example, the
genre, length, type (e.g., advertisement, movie), or another
classifier. Similarly, the ML training module 208 can be trained
classify a passenger's preferences with similar classifier. After
obtaining the information, the decision making module 210 can, for
example, match the classifiers to determine which multimedia
content to display to the passenger.
In some embodiments, the media metadata and pricing storage module
212 can store data related the multimedia content available to the
airplane 202. For instance, particular multimedia content can be
available after the passenger has paid for it. In some embodiments,
the media metadata and pricing storage module 212 can also
communicate with the ML training module 208. For example, one of
the classifiers can be a price classifier (e.g., expensive). ML
training module 208 can account for the preference of the passenger
to view multimedia content associate with a certain price (e.g.,
free, expensive).
The model generation module 216 can, in some cases, communicate
with the ML training module 208 to obtain the training data. After
which, the model generation module 216 can generate a training
model, which can predict a passenger's preferences. Alternatively,
the ML training module 208 and model generation module 208 can be
part of a single module. The advertisement management module 214
can determine pricing and slots of advertisements during the
presentation of the selected multimedia content. The advertisement
management module 214 can be based on, for example, the OneMedia
platform developed by Panasonic Avionics Corporation, headquartered
in Lake Forest, Calif.
In some embodiments, the advertisement management module 214 can
optimize the length and ratings of an advertisement based on how
users interact with the advertisement. The interaction can include,
for example, pausing, fast forwarding, rewinding, skipping, and/or
viewing. In general, the advertisement management module 214 can
optimize an advertisement in order to minimize advertisement space
(e.g., length and space on screen) while also maximizing views.
This concept is perhaps best described in reference to FIG. 2B.
FIG. 2B depicts two exemplary techniques for optimizing an
advertisement. Graph 220 depicts the relationship between the
length of an advertisement and the completion rate. The x-axis of
graph 220 is the length of an advertisement in minutes (referred to
as "creative_total_duration" in FIG. 2B) and the y-axis is the
completion rate (referred to as "Average of Completion_rate" in
FIG. 2B), where 1 means the entirety of the advertisement was
viewed. For example, when the advertisement is six minutes long,
the completion rate is 1 and when the advertisement is twelve
minutes long, the completion rate is approximately 0.25. Based on
this information, the advertisement management module 214 can
determine the optimal length of the advertisement (e.g. six
minutes). In this case, an optimal advertisement is one that is
completely viewed by the greatest number of users (e.g., passengers
aboard a commercial vehicle).
A second technique is shown in approach 222. Approach 222 includes
neighborhood formation, where the k most like minded users in the
system are found. In some embodiments, the system can be an entire
commercial passenger vehicle or a portion of the commercial
passenger vehicle. A likeminded user can be a user which, for
example, has similar preference or usage characteristics (e.g.,
similar completion rates or skipping of similar content). In
approach 222, users U.sub.2, U.sub.8, and U.sub.9 are found to be
similar.
Subsequently, a recommendation is generated. Namely, the
advertisement management module 214 can determine that items
I.sub.1 and I.sub.9 are not yet purchased (or viewed) by U.sub.8.
Based on this determination, the advertisement management module
214 can predict the possibility of U.sub.8 purchasing (or viewing)
I.sub.1 and I.sub.9. The prediction can include taking the weighted
sum based on, for example, the similarity between the items and/or
the users. In this manner, the advertisement management module 214
can optimize advertisements.
Returning the FIG. 2A, the user profiles and decision making data
storage module 218 can store the results of the preference system
in a profile form. For example, the module 218 can develop profile
for each passenger aboard the airplane 202. Thus, the decision
module 210, for example, can refer to the profile of each user to
determine new multimedia content to display.
FIG. 3 shows multiple seat back monitors 302a-n operating with a
server 304. Each of the plurality of seat back monitors 302a-n
communicate the passenger preferences with the server 304. In this
context, server 304 can include the components of preference system
204. Further, the server 304 can be on board the commercial vehicle
(e.g., airplane), and optionally, communicate with a ground server
to do pre-flight analysis, as mentioned above. The pre-flight
analysis can include analyzing passenger data such as frequent
flier data, demographic information (e.g., age), and other data to
generate a preference prediction for each passenger. The server 304
can use pre-flight analysis as a factor in performing the
preference analysis during the flight, as mentioned in conjunction
with FIG. 2.
For example, a particular passenger can be associates with monitor
302a aboard an airplane. Prior to taking off the server 304 may
have performed pre-flight analysis for the particular passenger to
determine their preferences. The pre-flight analysis may have
included data of the frequent flier data. The frequent flier may
indicate that the particular passenger routinely visits Hawaii.
Based on this data, the server 304 can determine to display an
advertisement for a beach front resort in Hawaii when the
particular passenger initially boards the airplane.
Over time, as the particular passenger interacts with the monitor
302a, the server can glean more information regarding the
particular passenger's preferences. For instance, the particular
passenger can poorly rate the advertisement for a beach front
resort in Hawaii. Due to the poor rating, the server 304 can adjust
the previously determined preferences. The adjustments can be, for
example, to no longer display Hawaii related content,
advertisements, or beach related advertisements.
In some embodiments, the analysis can account for current seat
passenger prior purchases and service requests recorded in a
passenger electronic manifest including: geographical location and
preferences; historical purchases including food, beverage, luxury
items; prior travel; future travel; passenger status; airplane
company passenger loyalty program likes and dislikes; type of
travel; type of destination; length of flight to destination;
flight destination location; information on aircraft flight
patterns; an aircraft flight duration, or the like; and current
passenger requests.
In some embodiments, the analysis required to determine a
passenger's preferences can be done without placing a web cookie on
the device associated with the passenger. Generally, a web cookie
is a small piece of data stored on a person's computer to remember
stateful information or the record the user's browsing data.
Instead of placing web cookies, the analysis described herein can
be performed based on interaction by the passenger with the
seatback device or PED, or information gathered based on the
passenger (e.g., frequent flier data). More specifically, the
analysis described herein can be performed with duration centric
tracking. For instance, for the duration of a flight.
Further, the analysis done for each seatback device (e.g., monitors
302a-n) can be cleared at the end of each passenger's travel. Thus,
a subsequent passenger that uses a seatback device may not be
presented with multimedia content based on the prior passenger's
preferences. For example, when an airplane reaches a destination,
the server 304 can be cleared of preference data.
FIG. 4 shows an exemplary system for presenting passenger
preference based content 406. Airplane 402 can be traveling from
California to Florida. The passenger can have a preference for
swimming. The preference can be determined based on for example,
that the passenger has previously flown to beachfront location,
highly rated videos regarding aquatic sports, the destination, or
the like. The machine learning algorithms can use the data (e.g.,
pre-flight analysis and onboard interactions) to determine that an
advertisement 404 for a Miami Beach Resort aligns the passenger's
interests. As such, the seatback display associated with the
passenger, can display the advertisement for Miami.
FIGS. 5A-B shows two exemplary actions from a passenger to indicate
a preference. In particular, FIG. 5A depicts a skip feature 502 and
FIG. 5B depicts a rating system 504. In FIG. 5A, a server, for
example, can send commands for the seatback monitor to display a
skip option. The commands can include, for example, when the skip
feature should be display, for long it should be displayed, or
where on the monitor it should be displayed. As mentioned above, if
the passenger elects to skip viewing the multimedia content, the
server can infer that the preferences for the passenger should
indicate a dislike towards the content being currently
displayed.
Similarly, the rating feature in FIG. 5B can be depicted at various
times, locations on the monitors, and can include multiple types of
graphics. For example, as depicted in FIG. 5B, a star rating system
is displayed, where selecting 5-stars indicates that the passenger
liked the content, whereas a 1-start would indicate dislike. In
some embodiments, the skip feature can be used while the content is
being displayed and the rating system can be displayed after the
content is finished playing. For example, a five minute may be
playing, and the skip feature may be displayed for the first thirty
seconds and the rating system may be display at the end of the five
minutes. Alternatively or additionally, the rating system may be
displayed after the passenger presses the skip option. Although
FIG. 5A-B depict two options as ways the passenger can interact to
indicate preferences, other options are also possible.
Exemplary Methodology
FIG. 6 shows an exemplary flowchart 600 of a method for delivering
passenger preference based content in a commercial passenger
vehicle. The method can be implemented by a server on board a
commercial passenger vehicle. In some embodiments, the method can
be implemented by a processor onboard a commercial passenger
vehicle, where the processor executed instructions stored in memory
(e.g., non-transitory computer-readable medium) on board the
commercial passenger vehicle. For example, the method can be
implemented by a mobile device that belongs to a passenger or a
seatback monitor, which is located on ahead rest facing the
passenger. Further, the method can be implemented on a device that
is collocated with a plurality of other devices (e.g., on an
airplane).
At block 602, the method includes performing training to obtain an
entertainment preference of at least one passenger of the
commercial vehicle. The training can be completed during a first
portion of a predetermined nominal duration (e.g., length of a
flight). Further, the training can help determine a duration of the
multimedia content. In some cases, the duration can be less than a
portion of the predetermined nominal duration.
For example, the algorithm optimizes advertisement length,
advertisement position/timing during the flight and generates
"value" advertisement time segment pricing (e.g., advertisement
slots, A, B, C, D). For example, Slot A can be 7 seconds in
duration for a Company D for Hotels at a beginning of a flight,
Slot B can 5 seconds in duration for Liquor Company A at 20 minutes
after take-off and during beginning of Comedy Movie A, Slot C can
10 seconds in duration for a Rental Car Company after drink/food
service and during the middle of Western Movie B, and Slot D can be
9 seconds long for a Ride Share Company 20 minutes before landing
and at the ending of Drama Television Program C.
The training can include, at block 604, sending, to a media
playback device associated with the passenger and on board the
vehicle, a multimedia content. In some embodiments, the method
further includes tracking the multimedia content being displayed on
the media playback device and applying machine learning algorithms
(e.g., collaborative filtering) to develop a trained model. The
trained model can be operable to determine a similarity between the
multimedia content and the entertainment preference of the
passenger.
Alternatively or additionally, the entertainment preference of the
passenger can be based on, for example, (1) social analytics or
press releases; (2) an interaction of the at least one passenger
with social media content and/or the multimedia content; (3) an
origin and/or destination of the commercial passenger vehicle;
and/or (4) the predetermined nominal duration.
At block 606, the method includes receiving an interaction by the
passenger on the multimedia content. The interaction can be, for
example, skipping portions of the multimedia content or rate the
multimedia content. The method can further include prompting the
passenger to rate the multimedia content being displayed on the
media playback device. Another factor can be performing a plurality
of trainings to obtains the entertainment preferences of the other
passengers on board the vehicle and collocated with the
passenger.
Based on the interaction and trained, the method includes
determined whether the entertainment preference needs to be
updated, at block 608. For example, if the trained model indicates
that there is a similarity between the multimedia content and the
entertainment preference, then the multimedia content can be
continued to be displayed. If, however, there isn't a similarity,
an update to entertainment preference can be performed, at block
610. Similarly, if the passenger highly rates the multimedia
content, then an update may not necessary. If the rating is low,
then an update can be performed.
Updating the entertainment preference can include, for example,
applying item-based and/or user-based techniques. In some
embodiments, updating the entertainment preference can include
determining, by the server, one or more passengers onboard the
commercial passenger vehicle with entertainment preferences similar
to the at least one passenger; identifying, by the server, other
multimedia content which has not been displayed by the media
playback device and has been viewed by the one or more passengers;
and determining, by the server, a probability of the at least one
passenger viewing the other multimedia content.
If an update is performed, the updated entertainment preference can
be transmitted to, for example, the on board server. If an update
is not performed, the multimedia content associated with the
entertainment preference can continue to be displayed. In some
embodiments, at the end of the predetermined nominal duration, the
entertainment preference of the passenger can be deleting from the
server and/or device on board the vehicle.
Exemplary Computing System
FIG. 7 is a block diagram illustrating a diagrammatic
representation of a machine in the example form of a computer
system operable to perform aspects of the disclosed technology. The
computing system 700 may be seatback device, a PED, a server
computer, a client computer, a personal computer (PC), a user
device, a tablet PC, a laptop computer, a personal digital
assistant (PDA), a cellular telephone, an iPhone, an iPad, a
Blackberry, a processor, a telephone, a web appliance, a network
router, switch or bridge, a console, a handheld console, a
(handheld) gaming device, a music player, any portable, mobile,
handheld device, wearable device, or any machine capable of
executing a set of instructions, sequential or otherwise, that
specify actions to be taken by that machine.
The computing system 700 may include one or more central processing
units ("processors") 702, memory 704, input/output devices 706
(e.g., keyboard and pointing devices, touch devices, display
devices), storage devices 708 (e.g., disk drives), and network
adapters 710 (e.g., network interfaces) that are each connected to
an interconnect 712. The interconnect 712 is illustrated as an
abstraction that represents any one or more separate physical
buses, point to point connections, or both connected by appropriate
bridges, adapters, or controllers. The interconnect 712, therefore,
may include, for example, a system bus, a peripheral component
interconnect (PCI) bus or PCI-Express bus, a HyperTransport or
industry standard architecture (ISA) bus, a small computer system
interface (SCSI) bus, a universal serial bus (USB), IIC (I2C) bus,
or an Institute of Electrical and Electronics Engineers (IEEE)
standard 1394 bus (e.g., Firewire).
The memory 704 and storage devices 708 are computer-readable
storage media that may store instructions that implement at least
portions of the various embodiments. In addition, the data
structures and message structures may be stored or transmitted via
a data transmission medium (e.g., a signal on a communications
link). Various communications links may be used (e.g., the
Internet, a local area network, a wide area network, or a
point-to-point dial-up connection). Thus, computer readable media
can include computer readable storage media (e.g., non-transitory
media) and computer readable transmission media.
The instructions stored in memory 704 can be implemented as
software and/or firmware to program the processor 702 to carry out
actions described above. In some embodiments, such software or
firmware may be initially provided to the computing system 700 by
downloading it from a remote system through the computing system
700 (e.g., via network adapter 710).
The various embodiments introduced herein can be implemented by,
for example, programmable circuitry (e.g., one or more
microprocessors, programmed with software and/or firmware), or
entirely in special-purpose hardwired circuitry (e.g.,
non-programmable circuitry), or in a combination of such forms.
Special-purpose hardwired circuitry may be in the form of, for
example, one or more application-specific integrated circuits
(ASICs), programmable logic devices (PLDs), field-programmable gate
array (FPGAs), etc.
CONCLUSION
The embodiments set forth herein represent the necessary
information to enable those skilled in the art to practice the
embodiments and illustrate the best mode of practicing the
embodiments. Upon reading the description in light of the
accompanying figures, those skilled in the art will understand the
concepts of the disclosure and will recognize applications of these
concepts that are not particularly addressed herein. These concepts
and applications fall within the scope of the disclosure and the
accompanying claims.
The above description and drawings are illustrative and are not to
be construed as limiting. Numerous specific details are described
to provide a thorough understanding of the disclosure. However, in
certain instances, well-known details are not described in order to
avoid obscuring the description. Further, various modifications may
be made without deviating from the scope of the embodiments.
As used herein, unless specifically stated otherwise, terms such as
"processing," "computing," "calculating," "determining,"
"displaying," "generating," or the like, refer to actions and
processes of a computer or similar electronic computing device that
manipulates and transforms data represented as physical
(electronic) quantities within the computer's memory or registers
into other data similarly represented as physical quantities within
the computer's memory, registers, or other such storage medium,
transmission, or display devices.
Reference herein to "one embodiment" or "an embodiment" means that
a particular feature, structure, or characteristic described in
connection with the embodiment is included in at least one
embodiment of the disclosure. The appearances of the phrase "in one
embodiment" in various places in the specification are not
necessarily all referring to the same embodiment, nor are separate
or alternative embodiments mutually exclusive of other embodiments.
Moreover, various features are described which may be exhibited by
some embodiments and not by others. Similarly, various requirements
are described which may be requirements for some embodiments but
not for other embodiments.
The terms used in this specification generally have their ordinary
meanings in the art, within the context of the disclosure, and in
the specific context where each term is used. Certain terms that
are used to describe the disclosure are discussed above, or
elsewhere in the specification, to provide additional guidance to
the practitioner regarding the description of the disclosure. For
convenience, certain terms may be highlighted, for example using
italics and/or quotation marks. The use of highlighting has no
influence on the scope and meaning of a term; the scope and meaning
of a term is the same, in the same context, whether or not it is
highlighted. It will be appreciated that the same thing can be said
in more than one way.
Consequently, alternative language and synonyms may be used for any
one or more of the terms discussed herein, nor is any special
significance to be placed upon whether or not a term is elaborated
or discussed herein. Synonyms for certain terms are provided. A
recital of one or more synonyms does not exclude the use of other
synonyms. The use of examples anywhere in this specification
including examples of any term discussed herein is illustrative
only and is not intended to further limit the scope and meaning of
the disclosure or of any exemplified term. Likewise, the disclosure
is not limited to various embodiments given in this
specification.
Without intent to further limit the scope of the disclosure,
examples of instruments, apparatus, methods and their related
results according to the embodiments of the present disclosure are
given above. Note that titles or subtitles may be used in the
examples for convenience of a reader, which in no way should limit
the scope of the disclosure. Unless otherwise defined, all
technical and scientific terms used herein have the same meaning as
commonly understood by one of ordinary skill in the art to which
this disclosure pertains. In the case of conflict, the present
document, including definitions will control.
From the foregoing, it will be appreciated that specific
embodiments of the invention have been described herein for
purposes of illustration, but that various modifications may be
made without deviating from the scope of the invention.
Accordingly, the invention is not limited except as by the appended
claims.
From the foregoing, it will be appreciated that specific
embodiments of the invention have been described herein for
purposes of illustration, but that various modifications may be
made without deviating from the scope of the invention.
Accordingly, the invention is not limited except as by the appended
claims.
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